What Makes Threat Detection Advanced?
Threat detection demands three key processes: intelligence gathering, threat verification, and response.
- Intelligence Gathering: Collect raw data (e.g., logs, network events) using SIEM and AI to identify anomalies.
- Threat Verification: Validate suspicious activity by cross-referencing threat intelligence, applying risk scoring, and reducing false positives.
- Response: Mitigate confirmed threats through automated actions and incident response protocols to isolate and remediate incidents.
Throughout the mid-2000s, tools like Security Information and Event Monitoring (SIEM) were able to automatically build log data points into individual streams of intelligence.
Analysts would then be alerted when strange strings of logs were detected.
However, SIEM tools had some consistent problems – false positives were rife as user actions and applications changed over time, and it still placed a burden on analysts to verify and react to every alert.
Advanced threat detection tools fix this by automatically comparing generated logs against the relevant contexts within an enterprise. This is made possible with AI, which allows advanced threat detection to assess your networks’ and endpoints’ requests against external threats and internal day-to-day behaviors.
Real-Life Example:
Rather than throwing an alert whenever a user logs in from a new device, advanced tools verify their legitimacy by requesting MFA and log this new device against a user’s underlying risk score. Should this new device then start requesting abnormal resources, it then issues an alert.
As alerts are cross-referenced before they hit analysts’ workflows, they’re immediately actionable.
The Importance of Advanced Threat Detection for Modern Cyber Threats
Threat detection has a lot to contend with advanced threats like:
Plus, while these attacks often originate outside a company, they can also be leveraged by insider threats – typically current or former employees with privileged knowledge of the business. Attackers may sometimes gain access months or even years before deploying a full-scale attack.
Because of this, threat detection has to continuously monitor every corner of every network. Achieving this now often demands a full suite of individual tools.
4 Key Advanced Threat Detection Tools
Since threat detection is such a large field, it’s useful to break it into bitesize components.
This is done by using multiple cybersecurity tools. These tools collectively make up the majority of organizations’ threat detection and response tech stacks.
Note: Not all of these need to be deployed as individual tools: an increasing number of security providers are pulling threat detection capabilities into single, cohesive platforms
#1: SIEM for Log Analysis
SIEM tools enhance threat detection by collecting, aggregating, and analyzing log data from a wide array of sources, such as:
- Servers
- Endpoints
- Firewall
- Applicazioni
They normalize this data into a consistent structure, making it easier to identify patterns and correlations. By aggregating logs into a centralized platform, SIEM tools use correlation rules to link seemingly unrelated events—such as multiple failed login attempts followed by a data exfiltration attempt—into actionable insights.
These tools monitor log data in real-time, leveraging threat intelligence feeds to compare activity against known Indicators of Compromise (IoCs). When anomalies or predefined thresholds are detected, SIEMs generate alerts to notify security teams of potential threats.
Plus, they retain log data for historical analysis, letting organizations:
- Identify trends
- Perform forensic investigations
- Comply with regulatory requirements.
By combining real-time monitoring, anomaly detection, and actionable alerts, SIEM log analysis provides you with a powerful framework for proactive and reactive threat detection.
#1: NDR for Network Analysis
NDR solutions continuously monitor east-west and north-south network traffic – providing deep visibility into internal communications and external connections. By correlating data across network segments, NDR tools can identify malicious activity that might otherwise go unnoticed, such as:
- Lateral movement
- Infiltrazione di dati
- Command-and-control (C2) communications.
This visibility enables organizations to spot threats that bypass endpoint or perimeter defenses.
Unlike traditional security tools that rely on signatures or predefined rules, NDR identifies suspicious files and activity by recognizing patterns, anomalies, and behaviors that deviate from the norm. This allows NDR systems to detect both known and unknown threats.
#3: Third-Party Threat Intelligence
Understanding the attacks being leveraged against your industry peers can help refine your own defenses.
This is why many cybersecurity tools come with inbuilt threat intel – the more wide-ranging and to-the-minute this intelligence is, the higher fidelity you get into your own risk.
#4: XDR for Endpoint Analysis
Extended Detection and Response (XDR) offers continuous monitoring of endpoints for suspicious behaviors, like:
- Unauthorized access attempts
- Unusual file modifications
- Unexpected process executions.
By combining this data with stuff like network traffic, it can uncover threats that exploit endpoint vulnerabilities as part of broader attack campaigns. For instance, XDR can link an endpoint anomaly to network-based lateral movement or a phishing email that delivered malware.. .
AI and Machine Learning in Threat Detection
AI and machine learning have become non-negotiable pieces of the threat detection architecture. This is because they offer a number of unique capabilities.
Unmatched Speed and Precision
AI is custom-built to process vast volumes of data. It’s been developed hand-in-hand with new ways of managing and transporting data, thanks to the introduction of data lakes and warehouses. With endless data now both collectable and transferrable, algorithms can ingest huge data points at unprecedented speed, including:
- Network traffic
- System logs
- User activity
Adaptive Defenses
With cybercriminals constantly innovating new tactics, AI’s ability to learn and evolve is critical.
By analyzing historical attacks and threat intelligence, AI-powered systems can build a baseline of user and network behaviors. Crucially, however, it’s possible for AI models to follow set success criteria: in the pursuit of reaching these, an AI model can adapt and adjust its own parameters.
This translates to more precise detection capabilities that evolve in response to an enterprise’s own changes.
Enhanced Efficiency and Accuracy
Since security teams face such an overwhelming volume of data, it’s challenging to discern genuine threats from time-wasting false positives.
AI automates repetitive security tasks, and therefore gives analysts a more complete picture of every incident.
Implement Advanced Threat Detection with Check Point Infinity
Achieving advanced threat detection doesn’t have to be complex: Check Point’s Infinity platform provides full-stack security across endpoints, mobiles, databases, networks, and email inboxes with a core set of services. Operating off a single platform, lean security teams can leverage the full weight of AI-powered threat detection, and start implementing automated response capabilities to drive even greater efficiency.
Start exploring Check Point Infinity by watching a demo.
For more network-specific threat detection capabilities, check out Quantum – it’s a world-class example of AI detection capabilities that combine rich rule-based prevention with an AI engine that examines millions of runtime parameters.
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